Prof. Dr. Manfred Claassen is group leader at the Institute of Molecular Systems Biology at ETH Zurich and co-founder of Scailyte AG. Read about his view on the current state and the future of single-cell technology.
- What kind of research is your laboratory involved in at the moment?
Our lab aims at elucidating the composition of heterogeneous cell populations and how these implement function in the context of cancer, immune biology and translational science by jointly evaluating single cell and genome wide measurements. Our scientific approach to these goals builds on statistical and machine learning methodology to describe biological systems, learn these descriptions from data and to design experiments to validate hypotheses following from computational analyses.
- What single-cell profiling techniques do you use in your laboratory?
We have so far focused on mass- and flow cytometry and have started to adopt single-cell RNA sequencing and imaging techniques.
- How do you think single-cell technology is influencing the transition towards precision medicine from generalized medicine?
High-dimensional single-cell technologies deliver a new type of information that enables us to systematically define cell identity biomarkers, i.e. cell subpopulations that are prognostic for disease progression, predictive for therapy response and possibly theragnostic. Cell identity biomarkers can be thought of as an information-rich generalization of conventional biomarkers, where a, possibly new cell type with a non-trivial molecular signature carries the clinically relevant information, instead of a single molecule. Immune cell signatures are promising cell identity biomarkers, encoding information about subtle personal disease states that, for instance, recently have been proven to predict immunotherapy response in melanoma. Generally, I expect single-cell technologies and in particular, the resulting information-rich cell identity biomarkers to have great potential to enable precision medicine for complex diseases such as cancer and autoimmune diseases.
- How advanced are the current tools for single-cell data analysis?
Single-cell data analysis traditionally aimed at describing all cell types present in a biological or clinical specimen and therefore typically resorted to unsupervised visualization and clustering of the data. An increasing amount of studies in basic and translational science are now aiming at associating the single-cell data with clinically relevant information such as disease activity or therapy response. Conventional unsupervised analysis is only of limited use to achieve this goal, and particularly fails to find subtle disease associated cell signatures. Only recently, suitable supervised analysis approaches have been proposed to sensitively tap single-cell data to detect such subtle disease associated cell signatures.
- What are the limitations of single-cell technology in its current state that need to be addressed?
High dimensional single-cell data, such as mass cytometry and in particular single-cell RNA sequencing data are quite noisy and thereby precluding the detection of subtle differences. Reduction of noise sources such as drop-outs in single-cell RNA sequencing will counteract this limitation.
For explorative studies, such as biomarker discovery, it is helpful to precisely measure a large number of molecular components per cell. While single-cell proteomic technologies allow measuring 40+ targets, it is necessary to specify these targets a priori and frequently constrained by the availability of suitable antibodies. Technological solutions to overcome, or at least alleviate this limitation would certainly boost the potential of single-cell proteomic approaches in explorative studies.
However, above desirable technological advancements will render data interpretation even more challenging and require that development of automatic techniques keeps up with enabling researchers to tap the full potential of their ever more informative single-cell data.
- How are academia-industry partnerships advancing the single-cell biology field? Can you give an example from your experience?
As pointed out before, data interpretation of high-dimensional single-cell data remains a major challenge. Many researchers generating such data do not have access to tools enabling them to interpret their data. I am a co-founder of Scailyte that aims at developing a software for automatic interpretation of high-dimensional single-cell data. At Scailyte we hope that this solution will be such a versatile tool.
- What is the future of single-cell technology and prediction of its success in patient healthcare?
Immune cells circulating in the blood have the potential to encode detailed information about the health and disease state of an individual. Single-cell technology has the potential to decode this information and allow developing simple blood based cell identity markers for a variety of complex diseases, such as cancer or autoimmune diseases, as well as for health monitoring and disease prevention.